Patent Application
20140169400
The present invention relates to automated systems and methods for monitoring user activities relating to health and well-being, and more specifically to systems and methods for approximating caloric energy intake and/or macronutrient composition.
There is a growing interest in developing automated systems capable of promoting health and well-being. For example, there are multiple mobile devices on the market focused on weight management, such as FitBit™ and BodyMedia™. These devices track daily caloric energy expenditure (EE) using 3-axis accelerometers and daily energy intake using PC and/or phone-based food logs. These food logs require the user to manually enter everything they eat and this information is converted to caloric energy intake (EI). These conventional systems may suffer from a variety of disadvantages. For example, it can be time consuming for a user to manually enter food consumption information into the system. It may also be challenging for a typical user to identify food type, serving size and other types of information that might be useful in characterizing consumed food. Even when food type, serving size and other similar types of information are available, it can be difficult for a user to obtain nutritional information for consumed food. Further, a user will often consume food in a location remote from the computer used to track food consumption. Accordingly, the user may be required to remember or take the time to write-down the information for later entry into the computer. As a result of these and other shortcomings, conventional systems are inconvenient to use and prone to significant error.
The present invention provides a system and method for approximating caloric energy intake and/or macronutrient composition using thermogenesis. In one embodiment, the system includes one or more sensors for tracking body temperature over a period of time to determine caloric energy intake and macronutrient composition of consumed food. In one embodiment, the system includes one or more temperature sensors located in positions that provide temperature data representation of core body temperature. For example, a sensor or network of sensors may be disposed on the user's body at one or more locations that permit a sufficiently accurate measurement of core body temperature.
In one embodiment, the sensor or sensors may be located in a device worn by the user, such as a wristband, anklet, ear piece or other similar device. In another embodiment, the sensor or sensors may be one or more epidermal skin sensors that can be applied directly to the user's skin. Epidermal skin sensors may be applied in essentially any location that provides accurate measurements. For example, epidermal skin sensors may be applied to the chest or in the armpit region of a user. In still other embodiments, a network of temperature sensors may include one or more sensors located in a device worn by the user and one or more epidermal skin sensors applied to the user's skin. If desired, a removable temperature sensor may be temporarily placed in contact with the skin when it is desirable to take temperature measurements. For example, a removable temperature sensor may be used to temporarily take the temperature of a user's forehead or scalp. As another example, a removable ear piece may be periodically place in an ear to collect temperature data. The ear piece need not be removable and may have the ability to pass sound using essentially the same circuitry as a hearing aid.
In one embodiment, the system is configured to develop a temperature profile for a meal from readings taken over a period time associated with that meal. For example, the temperature profile may start at the beginning of the meal and extend for a fixed period of time, such as six hours. The period of time need not, however, be fixed. For example, it may begin at the start of a meal and stop when the thermic effect of food has sufficiently fallen off. As another example, the period of time may stop if another meal is eaten before the time has expired. With this embodiment, the system may include an input device that allows the user to indicate the start of a meal. For example, the system may include a button, switch or other user input to flag the start of a meal. One alternative is to provide a device with integrated accelerometers or other motion sensors that allow the user to signal the start of a meal by making a specific gesture with the device. In one embodiment, the system is configured to determine caloric energy intake and/or macronutrient composition from the temperature profile based on the Thermogenic Maximum (TGM), Time to Thermogenic Maximum (TTM) and Total Thermogenic Response (TTR) of the temperature profile.
In one embodiment, the system may be configured to normalize body temperature readings to compensate for factors other than thermogenesis that might affect core body temperature. The number and types of normalization factors may vary from application to application. However, in one embodiment, the system may include one or more additional sensors that monitor physical activity levels of the user, ambient temperature, UV exposure and/or time of day. Other normalization factors may include factors such as menstrual cycle, wind speed and humidity level. The system may include a processor capable of normalizing body temperature readings based on the readings from the sensors for the normalization factors. In one embodiment, biometric and physiological data about the user, such as age, height, weight, gender, race and level of fitness, may be taken into consideration during normalization of the raw body temperature readings.
In one embodiment, the system includes a processor capable of processing raw temperature data, as well as other data (e.g. normalization data), to provide caloric energy intake and/or macronutrient composition. The processing capability may be integrated into a device that carries one or more sensors. That device may obtain all of the necessary temperature data from an on-board sensor (or sensors) or it may obtain at least some of the temperature data from remote sensors or remote devices that incorporate sensors. When obtaining temperature data remotely, the data may be communicated using conventional wireless communications protocols, such as Bluetooth, WiFi or NFC. The data may, however, be communicated in other ways, for example, using a corded connection or using communications techniques that are integrated into a wireless power supply, such as backscatter modulation. As an alternative to incorporating the processing capability into a device that carries one or more sensors, the processing capability may be integrated into a separate device. For example, the system may include a central device that is capable of receiving and processing temperature data and other relevant data (e.g. normalization data) from a network of remote sensors.
In one embodiment, the system includes a processor capable of generating a temperature profile and using the temperature profile to predict the macronutrient composition of consumed food. The system may include data storage for storing user calibration data useful in characterizing macronutrient composition based on temperature profile. The calibration data may be an algorithm (or collection of algorithms) or it may be a table or other form of data collection that allows information collected from normalization sensors to be converted into an adjustment for the raw temperature readings. In one embodiment, the user calibration data will represent the results of calibration tests conducted on the user. In other embodiments, the user calibration data may represent the results of calibration tests conducted on a test group. In some embodiments, the user calibration data may represent the combined results of calibration tests conducted on the user and a test group. The calibration data may include temperature collected in connection with the consumption of one or more meals of known macronutrient composition.
In one aspect, the present invention provides a method for determining caloric energy intake including the steps of: (a) collecting data representing a user's body temperature over a period of time, (b) normalizing the raw body temperature data and (c) determining caloric energy intake as a function of the normalized body temperature data. The method may include the steps of tracking and quantifying changes in an individual's body temperature throughout a day (specifically after a meal) and normalizing and correcting the raw temperature data by combining sensors that monitor activity levels, ambient temperature, UV exposure, and time of day. The present invention may employ methods and equations that allow temperature profiles after meal consumption to predict the macronutrient composition of a meal.
In one embodiment, the step of determining caloric energy intake may include the steps of developing equations calibrated for the user. The calibrated equations may be developed by having the user consume a plurality of meals of known macronutrient composition, developing a body temperature profiles representing the TEF for each of the consumed meals and calibrating the caloric energy intake equations as a function of the TGM, TTM and TTR of the temperature profiles. In one embodiment, each of the plurality of meals is provided with different percentages of the different macronutrients.
The present invention provides simple and effective systems and methods for approximating caloric energy intake and/or macronutrient composition. The systems and methods are based on thermogenesis and therefore can be implemented using relatively inexpensive and non-invasive temperature sensors. The present invention provides systems and methods that overcome the shortcomings of conventional systems that require manual entry of information relating to caloric energy intake. The systems and methods may incorporate normalization of the raw body temperature readings to improve the accuracy of the caloric energy intake and/or macronutrient composition approximations. The systems and methods may be capable of normalizing for essentially any environmental factors that might impact body temperature readings. The systems and methods are capable of implementing one or more normalization factors to improve the accuracy of the approximations, as desired. The systems and methods may be capable of calibrating for user-specific variations, such as metabolic function, age, height, weight, gender, race and level of fitness, to improve the accuracy of the caloric energy intake and/or macronutrient composition approximations. The systems and methods allow implementation of the normalization and calibration capabilities at different levels based on various factors, such as system cost, desired accuracy and user convenience.
These and other features of the invention will be more fully understood and appreciated by reference to the description of the embodiments and the drawings.
Before the embodiments of the invention are explained in detail, it is to be understood that the invention is not limited to the details of operation or to the details of construction and the arrangement of the components set forth in the following description or illustrated in the drawings. The invention may be implemented in various other embodiments and of being practiced or being carried out in alternative ways not expressly disclosed herein. Also, it is to be understood that the phraseology and terminology used herein are for the purpose of description and should not be regarded as limiting. The use of “including” and “comprising” and variations thereof is meant to encompass the items listed thereafter and equivalents thereof as well as additional items and equivalents thereof. Further, enumeration may be used in the description of various embodiments. Unless otherwise expressly stated, the use of enumeration should not be construed as limiting the invention to any specific order or number of components. Nor should the use of enumeration be construed as excluding from the scope of the invention any additional steps or components that might be combined with or into the enumerated steps or components.
A system 10 for approximating caloric energy intake and/or macronutrient composition of consumed food is shown in
Although the embodiment of
Thermogenesis is the process of heat production in animals. In warm blooded animals heat is generated by three main thermogenic processes: i) exercised induced, ii) non-exercised induced, and iii) diet induced. The later form of thermogenesis is often referred to as the thermic effect of food (TEF). Thermic effect of food is the increase in thermogenesis after the consumption of macronutrients. This meal-induced thermogenesis can last for about 6 hours after the consumption of food.
The thermic effect of food can be measured using both direct and indirect calorimetry. Direct calorimetry measures the increase in whole body temperature that occurs after consumption of a meal. Whole body temperature changes can be measured by placing and individual into a metabolic chamber. Indirect calorimetry measures the amount of oxygen consumed and the amount carbon dioxide exhaled by an individual. This measurement technique results in an indirect measure of heat generated. This measurement is typically accomplished using a VO2/CO2 metabolic chart.
Generally speaking, the increase in magnitude of thermogenesis in response to a meal is dependent on both the number of calories consumed and the macronutrient composition of the meal. The three macronutrients that can be present in food are proteins, fats, and carbohydrates. Protein has the highest thermogenic effect, followed by carbohydrates, and then fats. For example,
As noted above, the present invention may be implemented in a wide variety of devices or network of devices/components. In the embodiment of
The device 12 may also include a variety of additional components intended to provide additional capabilities, including, for example, circuitry configured to receive and transmit data and information with other system components, such as other devices and/or remote sensors. The device 12 of
Referring again to
Body temperature sensor(s) 16 and ambient temperature sensor(s) 14 may be incorporated into the device 12 and/or may be separate remote temperature sensors that are capable of providing temperature data to the device 12, for example, using wired or wireless communications, such as Bluetooth, WiFi, NFC or RF communications. When body temperature sensors are incorporated into the device 12, a temperature sensor may be positioned on an interior surface that generally remains in contact with the user's skin to periodically collect body temperature readings. The device 12 may alternatively include a temperature sensor that is on an external surface and is placed in contact with the body each time a temperature reading is desired. When remote body temperature sensors are included, they may be placed where they will provide measurements that are most similar to core body temperature. For example, remote temperature sensors 16 may be located on the chest or forehead, or in the arm pit. Remote temperature sensors 16 may additionally or alternatively be placed over body organs, such as the kidneys, stomach or liver.
Similarly, the ambient temperature sensor(s) 14 may be positioned where it will provide temperate readings that most accurately represent ambient temperature. For example, an ambient temperature sensor may be located on the exterior of the device 12 where it is exposed to ambient air and isolated from body temperature as much as possible. As another example, the ambient temperature sensor may be a remote sensor (e.g. separate from the device 12) that is located away from the user where it may provide a more accurate measure of ambient temperature. When the ambient temperature sensor is a remote sensor, it may be capable of communicating its readings to the device 12 or to a central device.
Examples of the temperature sensors include thermocouples, thermistors and resistance temperature detectors. Another example of a temperature sensor is an epidermal skin sensor. Epidermal skin sensors have been demonstrated and are now available commercially, for example, from mc10 Incorporated of Cambridge, Mass. Epidermal skin sensors are typically thin (˜25-75 μm), stretchable, and can be in conformal contact with human skin.
As noted above, the device 12 of
Another example of a remote sensor 60 is shown in
The present invention also provides method for approximating caloric energy intake and/or macronutrient composition of consumed food. In one embodiment, the method approximates caloric energy intake including the steps of: (a) collecting data representing a user's body temperature over a period of time, (b) normalizing the raw body temperature data and (c) determining caloric energy intake as a function of the normalized body temperature data. The method may include the steps of collecting core body temperature data during a period of time, for example, during a period of time beginning at the start of a meal. The method may include the step of generating temperature profiles after meal consumption to predict the macronutrient composition of a meal. The method may include the steps of normalizing and correcting the raw temperature data using data from sensors that monitor one or more of activity levels, ambient temperature, UV exposure, menstrual cycle and time of day.
As noted above, body temperature readings may be collected using one or more temperature sensors 16. The body temperate readings may be taken periodically over a period of time and may be used to develop a temperature profile. Alternatively, temperature readings may be taken periodically on a continuous-basis, rather than over a period of time. In one embodiment, the body temperature readings begin at the start of a meal and are taken periodically for a fixed period of time. In one embodiment, readings are taken for a period of six hours from the start of a meal. If a fixed-length period is used, the length of the period may vary from application to application. For example, the typical length of TEF for a given user may be determined through testing and that typical length may be used as the fixed-length for that user. Temperature readings need not be taken over a fixed period of time. For example, temperature readings may begin at the start of a meal and stop when the thermic effect of food has sufficiently fallen off. As another example, the period of time may stop if another meal is eaten before the time for the preceding meal has expired.
With this embodiment, the system may include an input device that allows the user to indicate the start of a meal. For example, the system may include a button, switch or other user input to flag the start of a meal. One alternative is to provide a device with integrated accelerometers or other motion sensors that allow the user to signal the start of a meal by making a specific gesture with the device. For example, a user may shake the device in a predetermined way to signal the start of a meal.
As discussed in more detail below, body temperature readings will be converted into energy intake using the equations described in
In this embodiment, the method includes the step of normalizing raw core body temperature readings for factors other than TEF that could impact those readings using additional device-based and networked sensors. For example, time of day will be accounted for with an internal clock; body temperature increases associated with activity could be normalized based on the reading of the activity sensor; body temperature changes associated with environmental temperature could be normalized using the ambient temperature sensor; and skin temperature increase due to exposure to sun could be normalized using a UV dosimeter.
Time of day may affect internal body temperature. For example, the body temperature of an individual may increase and decrease over the day. To accommodate for these variations, it may be desirable to adjust raw body temperature readings based on the time of day. In one embodiment, normalization for time of day may be achieved by adjusting the raw temperature readings based on a time-of-day temperature profile. The time-of-day profile may be developed by monitoring variations in the user's own internal body temperature over time under controlled conditions. These variations may be analyzed to develop an algorithm for converting time of day into an adjustment for the raw body temperature data. The algorithm may be mathematical formula that converts time of day into a number that can be added or subtracted from the raw body temperature. Alternatively, the algorithm may be a table or other arrangement of data that can be used to convert time of day into a number that can be used to normalize raw body temperature. As an alternative to running controlled tests with the user of the system, the time-of-day profile may be developed by monitoring time-of-day variations in the internal body temperature of a test group under controlled conditions. Again, the variations may be analyzed to develop an algorithm (e.g. formula or table) for converting time of day into an adjustment for the raw body temperature data. It should be noted that variations may differ in different groups of people. For example, age, gender, race, height, weight and level of fitness may have a material impact of the variations that occur over the time of day. Accordingly, different algorithms may be developed for different groups of people.
Physical activity may also affect internal body temperature. Heavy physical activity can significantly increase internal body temperature. Normalization for physical activity may be achieved by adjusting the raw temperature readings based on an activity temperature profile. The activity temperature profile may be developed by monitoring variations in the user's own internal body temperature during various levels of physical activity, as discussed above in connection with time-of-day normalization. Alternatively, the activity temperature profile may be developed by monitoring variations in the internal body temperature of a test group during various levels of physical activity under controlled conditions, as discussed above in connection with time-of-day normalization.
Body temperature will also vary with the temperature of the environment (e.g. ambient temperature). For example, a user's core body temperature will typically increase when the temperature of the environment increases. Similarly, a user's core body temperature may decrease in a colder environment. Normalization for ambient temperature may be achieved by adjusting the raw temperature readings based on an ambient temperature profile. The ambient temperature profile may be developed by monitoring variations in the user's own internal body temperature when subjected to different ambient temperatures, as discussed above in connection with time-of-day normalization. Alternatively, the ambient temperature profile may be developed by monitoring variations in the internal body temperature of a test group when subjected to different environmental temperatures under controlled conditions, as discussed above in connection with time-of-day normalization.
Exposure to the sun can also have a material impact on raw temperature readings. For example, increased exposure to the sun can materially increase skin temperature, which can affect the readings of temperature sensors that measure skin temperature. UV exposure can also increase core body temperature. As a result, it may be desirable to normalize raw temperature readings to compensate for UV exposure. Normalization for UV exposure may be achieved by adjusting the raw temperature readings based on a UV temperature profile. The UV temperature profile may be developed by monitoring variations in the user's own internal body temperature when subjected to different levels of UV exposure, as discussed above in connection with time-of-day normalization. Alternatively, the UV temperature profile may be developed by monitoring variations in the internal body temperature of a test group when subjected to different levels of UV exposure under controlled conditions, as discussed above in connection with time-of-day normalization.
The steps associated with normalization for physical activity will now be described in more detail. Looking at
Body temperature changes associated with environmental temperature could be normalized using the ambient temperature sensor. Finally, skin temperature increase due to exposure to sun could be normalized using a UV dosimeter. In this embodiment, both of these examples may be calibrated the same way where UV dose would be correlated to body temperature and a function would be developed so that whatever reading the UV dosimeter reported, could be converted to an adjustment for the raw body temperature readings.
Another method to normalize for different variables affecting body temperature is to create a temperature profile over time. In one embodiment, this may involve device 12 measuring body temperature many times in a day during predetermined times where it is known that activity and food is not affecting the measurement. The values can then be averaged and plotted over a certain time period. This plot would represent a temperature profile and it could be used to understand internal temperature shifts not based on activity or food. A woman's menstrual cycle is one example of this. Research has shown that body temperature changes over a woman's menstrual cycle, which can be seen in
Experience has revealed that TEF may vary from individual to individual. For example, factors such as metabolism rate may cause variations in the changes to core body temperature experienced as the result of food consumption. These variations may be specific to individual macronutrients. For example, different individuals may obtain different thermic effects from fats, proteins and/or carbohydrates. Other examples of factors that may be relevant to caloric energy intake and/or macronutrient composition include age, fitness level, weight, gender, race, menstrual cycle and biological rhythms.
A calibration period may be helpful to understand how specific individuals respond to different amounts of caloric intake, different macronutrients, and different macronutrient rations. In one embodiment, this calibration period includes the step of having an individual eat meals of know caloric amounts and macronutrient composition. These meals may be pre-packaged and provided or these meals may be based off of pre-determined recipes. In one embodiment, the calibration for caloric amount could take place as follows: i) obtain base-line sensor readings for temperature, activity and any other sensors of interest, ii) inform the device that meal 1 is going to be eaten (meal 1 would have a known amount of calories and a known macronutrient ratio), and iii) track sensors for up to six hours post meal. Although the sensors may be tracked for six hours, the numbers of hours may vary and need not be fixed. Temperature readings may be normalized relative to base-line measurements taking into account normalization for activity and ambient temperature.
The changes in body temperature resulting from meal consumption are expected to first increase to a maximum temperature and then gradually decrease to a temperature that is similar to the pre-meal temperature. These temperature changes are shown schematically in
Device calibration could be done by having an individual eat fixed amounts of a single macronutrient and measuring the three thermal characteristics. For example, to understand a specific individual's thermal response to protein the individual would eat a fixed amount of protein at defined time and TTR, TGM, and TTM would be measured. At a different time they would eat a different amount protein and the corresponding TTR, TGM, and TTM would be measured. In this embodiment, this measurement process would be repeated for three or more protein-only meals, where the amount of protein in each meal was different. From this calibration period, an individual's TTR(protein), TGM (protein), and TTM(protein) can be determined (See
TTR(protein)=m·protein(kcals)+b Eq. 1)
TGM(protein)=m·protein(kcals)+b Eq. 2)
TTM(protein)=m·protein(kcals)+b Eq. 3)
In a mixed macronutrient meal, the relative contribution of each macronutrient to the overall TTRmeal, TGMmeal, and TTMmeal may be understood. The relative contribution of each macronutrient to TTRmeal is described by λ. The relative contribution of each macronutrient to TGMmeal is described by γ. The relative contribution of each macronutrient to TTMmeal is a described by δ. The subscript on each these terms denotes the macronutrient (fat, or protein, or carbohydrate). In this embodiment, these λ, γ, and δ's are scalar weighting factors with values between 0 and 1. These values may have to be determined for each individual or may be general values for a defined population.
To determine the overall contribution of each macronutrient to the overall TTRmeal, TGMmeal, and TTMmeal, a matrix of calibration experiments may be performed. Testing three different combinations of macronutrients will provide enough equations to solve for the weighting factors. An example of the process of finding the calibration factors is shown below.
Referring now to
TGM(fat)=0.0025·fat(kcals) Eq. 4)
TGM(protein)=0.01·protein(kcals) Eq. 5)
TGM(carb)=0.005·carb(kcals) Eq. 6)
From here, three different meals would be selected to solve for the weighting factors γ1, γ2, and γ3 from
1.23(° C.)=(γfat·0.0025·225(kcals)+γprot·0.01·225(kcals)+γcarb·0.005·50(kcals))
0.53(° C.)=(γfat·0.0025·225(kcals)+γprot·0.01·50(kcals)+γcarb·0.005·225(kcals))
1.36(° C.)=(γfat·0.0025·50(kcals)+γprot·0.01·225(kcals)+γcarb·0.005˜225(kcals))
Converting this into matrix notation, Ax=b, gives the following matrix below.
Solving for the vector x gives the proportionality constants for the TGM equation.
This would then be done for each characteristic (i.e. TTR and TTM) in the same way to determine each set of proportionality constants from
After the macronutrient thermal characteristics are characterized and their respective weighting factors are known, the measured TTRmeal, TGMmeal, and TTMmeal of a meal of unknown caloric and macronutrient composition can be used in Equation 4, Equation 5, and Equation 6 below to determine the caloric content of each macronutrient.
TTRmeal=(λfat·TTR(fat)+λprot·TTR(protein)+λcarb·TTR(carb)) Eq. 7)
TGMmeal=(γfat·TGM(fat)+γprot·TGM(protein)+γcarb·TGM(carb)) Eq. 8)
TTMmeal=(δfat·TTM(fat)+δprot·TTM(protein)+δcarb·TTM(carb)) Eq. 9)
Combining Equations 7, Equation 8, and Equation 9 with the equations shown in
TTRmeal=(λfat·(m7·fat(kcals)+b7)+λprot·(m8·protein(kcals)+b8)+λcarb·(m9·carb(kcals)+b9)) Eq. 10)
TGMmeal=(γfat·(m1·fat(kcals)+b1)+γprot·(m2·protein(kcals)+b2)+γcarb·(m3·carb(kcals)+b3)) Eq. 11)
TTMmeal=(δfat·(m4·fat(kcals)+b4)+δprot·(m5·protein(kcals)+b5)+b5)+δcarb·(m6·carb(kcals)+b6)) Eq. 12)
As can be seen, the present invention provides some examples of systems and methods for approximating caloric energy intake and/or macronutrient composition. As described, determining macronutrient composition may be an integral part of determining caloric energy intake. The above description provides examples of systems and methods for normalizing raw temperature readings to compensate for factors other than TEF that might impact raw temperature readings. Similarly, the above description provides examples of systems and methods that include calibration to compensate for variations between individual users. The examples set forth are exemplary and should not be interpreted to limit the scope of the present invention to specific normalization and calibration systems and methods.
The above description is that of current embodiments of the invention. Various alterations and changes can be made without departing from the spirit and broader aspects of the invention as defined in the appended claims, which are to be interpreted in accordance with the principles of patent law including the doctrine of equivalents. This disclosure is presented for illustrative purposes and should not be interpreted as an exhaustive description of all embodiments of the invention or to limit the scope of the claims to the specific elements illustrated or described in connection with these embodiments. For example, and without limitation, any individual element(s) of the described invention may be replaced by alternative elements that provide substantially similar functionality or otherwise provide adequate operation. This includes, for example, presently known alternative elements, such as those that might be currently known to one skilled in the art, and alternative elements that may be developed in the future, such as those that one skilled in the art might, upon development, recognize as an alternative. Further, the disclosed embodiments include a plurality of features that are described in concert and that might cooperatively provide a collection of benefits. The present invention is not limited to only those embodiments that include all of these features or that provide all of the stated benefits, except to the extent otherwise expressly set forth in the issued claims. Any reference to claim elements in the singular, for example, using the articles “a,” “an,” “the” or “said,” is not to be construed as limiting the element to the singular.
| Number | Date | Country | |
|---|---|---|---|
| 61739271 | Dec 2012 | US |
